Germany: LK Rastatt (Baden-Württemberg)¶

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In [1]:
import datetime
import time

start = datetime.datetime.now()
print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}")
Notebook executed on: 07/03/2023 10:01:30 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Germany", subregion="LK Rastatt", weeks=5);
2023-03-07T10:01:45.303213 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 100 100 125 125 150 150 175 175 200 200 7-day incidence rate (per 100K people) 105.0 LK Rastatt, Germany, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 20 40 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0 0.1 0.2 0.3 0.4 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Rastatt cases daily growth factor Germany-LK Rastatt cases daily growth factor (rolling mean) Germany-LK Rastatt estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 2 2 4 4 R & growth factor (based on deaths) Germany-LK Rastatt deaths daily growth factor Germany-LK Rastatt deaths daily growth factor (rolling mean) Germany-LK Rastatt estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 1000 2000 3000 cases doubling time [days] Germany-LK Rastatt doubling time cases (rolling mean) 0.0 46.5 92.9 daily change Germany-LK Rastatt new cases (rolling 7d mean) Germany-LK Rastatt new cases 0.000 0.232 0.465 0.697 0.929 daily change Germany-LK Rastatt new deaths (rolling 7d mean) Germany-LK Rastatt new deaths 0.000 0.291 0.581 0.872
In [4]:
overview(country="Germany", subregion="LK Rastatt");
2023-03-07T10:02:03.820599 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1000 1000 2000 2000 7-day incidence rate (per 100K people) 105.0 LK Rastatt, Germany, last data point from 2023-03-06 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 200 400 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 1 2 3 daily change normalised per 100K May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Germany-LK Rastatt cases daily growth factor Germany-LK Rastatt cases daily growth factor (rolling mean) Germany-LK Rastatt estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2 2 4 4 R & growth factor (based on deaths) Germany-LK Rastatt deaths daily growth factor Germany-LK Rastatt deaths daily growth factor (rolling mean) Germany-LK Rastatt estimated R (using deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 2000 4000 6000 cases doubling time [days] Germany-LK Rastatt doubling time cases (rolling mean) Germany-LK Rastatt doubling time deaths (rolling mean) 0 465 929 daily change Germany-LK Rastatt new cases (rolling 7d mean) Germany-LK Rastatt new cases 0.000 2.323 4.646 6.969 daily change Germany-LK Rastatt new deaths (rolling 7d mean) Germany-LK Rastatt new deaths 0.0 151.1 302.3 453.4 deaths doubling time [days]
In [5]:
compare_plot(country="Germany", subregion="LK Rastatt", dates="2020-03-15:");
2023-03-07T10:04:27.933855 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.1 0.1 1 1 10 10 100 100 daily new cases (rolling 7-day mean) normalised by 100K people Daily cases (top) and deaths (below) for Germany: LK Rastatt LK Rastatt Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 0.001 0.001 0.01 0.01 0.1 0.1 1 1 daily new deaths (rolling 7-day mean) normalised by 100K people LK Rastatt Bayern Berlin Bremen Hamburg Hessen Nordrhein-Westfalen Sachsen-Anhalt
In [6]:
# load the data
cases, deaths = germany_get_region(landkreis="LK Rastatt")

# get population of the region for future normalisation:
inhabitants = population(country="Germany", subregion="LK Rastatt")
print(f'Population of country="Germany", subregion="LK Rastatt": {inhabitants} people')

# compose into one table
table = compose_dataframe_summary(cases, deaths)

# show tables with up to 1000 rows
pd.set_option("display.max_rows", 1000)

# display the table
table
Population of country="Germany", subregion="LK Rastatt": 232296 people
Out[6]:
total cases daily new cases total deaths daily new deaths
date
2023-03-06 114415 27 448 0
2023-03-03 114388 24 448 1
2023-03-02 114364 40 447 0
2023-03-01 114324 60 447 0
2023-02-28 114264 93 447 0
2023-02-27 114171 138 447 1
2023-02-24 114033 76 446 0
2023-02-23 113957 81 446 0
2023-02-22 113876 78 446 0
2023-02-21 113798 88 446 0
2023-02-20 113710 91 446 0
2023-02-17 113619 55 446 0
2023-02-16 113564 52 446 0
2023-02-15 113512 85 446 0
2023-02-14 113427 98 446 0
2023-02-13 113329 78 446 1
2023-02-10 113251 44 445 0
2023-02-09 113207 50 445 0
2023-02-08 113157 60 445 0
2023-02-07 113097 89 445 0
2023-02-06 113008 65 445 0
2023-02-03 112943 41 445 0
2023-02-02 112902 59 445 0
2023-02-01 112843 62 445 0
2023-01-31 112781 77 445 1
2023-01-30 112704 76 444 0
2023-01-27 112628 21 444 0
2023-01-26 112607 34 444 0
2023-01-25 112573 25 444 0
2023-01-24 112548 55 444 0
2023-01-23 112493 37 444 0
2023-01-20 112456 38 444 0
2023-01-19 112418 30 444 0
2023-01-18 112388 36 444 2
2023-01-17 112352 51 442 1
2023-01-16 112301 46 441 0
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2023-01-09 112085 73 441 1
2023-01-05 112012 43 440 2
2023-01-04 111969 47 438 1
2023-01-03 111922 59 437 0
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2022-12-19 111156 160 427 5
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Explore the data in your web browser¶

  • If you want to execute this notebook, click here to use myBinder
  • and wait (~1 to 2 minutes)
  • Then press SHIFT+RETURN to advance code cell to code cell
  • See http://jupyter.org for more details on how to use Jupyter Notebook

Acknowledgements:¶

  • Johns Hopkins University provides data for countries
  • Robert Koch Institute provides data for within Germany
  • Atlo Team for gathering and providing data from Hungary (https://atlo.team/koronamonitor/)
  • Open source and scientific computing community for the data tools
  • Github for hosting repository and html files
  • Project Jupyter for the Notebook and binder service
  • The H2020 project Photon and Neutron Open Science Cloud (PaNOSC)

In [7]:
print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and "
      f"deaths at {fetch_deaths_last_execution()}.")
Download of data from Johns Hopkins university: cases at 07/03/2023 09:31:22 and deaths at 07/03/2023 09:31:21.
In [8]:
# to force a fresh download of data, run "clear_cache()"
In [9]:
print(f"Notebook execution took: {datetime.datetime.now()-start}")
Notebook execution took: 0:06:00.239595